Untitled
利用するデータ
都道府県
都道府県と地方区分に関するデータは自作データ。サマライズ
df %>%
skimr::skim()| Name | Piped data |
| Number of rows | 87435 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| character | 19 |
| logical | 3 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patientId | 0 | 1.00 | 1 | 8 | 0 | 86028 | 0 |
| dateAnnounced | 0 | 1.00 | 10 | 10 | 0 | 251 | 0 |
| gender | 9503 | 0.89 | 1 | 1 | 0 | 2 | 0 |
| detectedPrefecture | 0 | 1.00 | 3 | 15 | 0 | 49 | 0 |
| patientStatus | 83666 | 0.04 | 8 | 23 | 0 | 8 | 0 |
| notes | 45594 | 0.48 | 1 | 270 | 0 | 39211 | 1 |
| mhlwPatientNumber | 86986 | 0.01 | 1 | 11 | 0 | 434 | 0 |
| prefecturePatientNumber | 8064 | 0.91 | 5 | 20 | 0 | 79362 | 0 |
| prefectureSourceURL | 56236 | 0.36 | 5 | 224 | 0 | 3424 | 0 |
| residence | 17322 | 0.80 | 1 | 38 | 0 | 1406 | 0 |
| sourceURL | 586 | 0.99 | 1 | 239 | 0 | 7037 | 0 |
| relatedPatients | 78292 | 0.10 | 2 | 259 | 0 | 5596 | 0 |
| knownCluster | 85123 | 0.03 | 3 | 88 | 0 | 222 | 0 |
| detectedCityTown | 65066 | 0.26 | 2 | 22 | 0 | 660 | 0 |
| cityPrefectureNumber | 65100 | 0.26 | 1 | 34 | 0 | 22326 | 2 |
| citySourceURL | 75707 | 0.13 | 9 | 317 | 0 | 3592 | 0 |
| deceasedDate | 85825 | 0.02 | 10 | 10 | 0 | 201 | 0 |
| deceasedReportedDate | 86335 | 0.01 | 10 | 10 | 0 | 175 | 0 |
| deathSourceURL | 86429 | 0.01 | 14 | 123 | 0 | 618 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| confirmedPatient | 0 | 1 | 0.98 | TRU: 86027, FAL: 1408 |
| charterFlightPassenger | 87421 | 0 | 1.00 | TRU: 14 |
| cruisePassengerDisembarked | 87424 | 0 | 1.00 | TRU: 11 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ageBracket | 0 | 1 | 33.62 | 23.13 | -1 | 20 | 30 | 50 | 100 | ▃▇▅▂▁ |
ラングリング
必要と思われる変量（フィーチャー）のみを適切な形式に変換し、地域区分でも分析できるように都道府県データと結合。
x <- df %>%
dplyr::mutate(dateAnnounced = lubridate::as_date(dateAnnounced),
ageBracket = forcats::as_factor(ageBracket),
gender = forcats::as_factor(gender),
patientStatus = forcats::as_factor(patientStatus),
residence = forcats::as_factor(residence),
cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE)) %>%
dplyr::select(dateAnnounced, ageBracket, gender, detectedPrefecture,
patientStatus, knownCluster, cluster) %>%
dplyr::left_join(prefs, by = c("detectedPrefecture" = "pref"))
xx %>%
skimr::skim()| Name | Piped data |
| Number of rows | 87435 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| Date | 1 |
| factor | 4 |
| logical | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| detectedPrefecture | 0 | 1.00 | 3 | 15 | 0 | 49 | 0 |
| knownCluster | 85123 | 0.03 | 3 | 88 | 0 | 222 | 0 |
| pcode | 1018 | 0.99 | 2 | 2 | 0 | 47 | 0 |
| 都道府県 | 1018 | 0.99 | 3 | 4 | 0 | 47 | 0 |
| 八地方区分 | 1018 | 0.99 | 4 | 5 | 0 | 8 | 0 |
| 広域圏 | 6014 | 0.93 | 3 | 3 | 0 | 8 | 0 |
| 通俗的区分 | 1018 | 0.99 | 2 | 3 | 0 | 11 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| dateAnnounced | 0 | 1 | 2020-01-15 | 2020-10-05 | 2020-08-06 | 251 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| ageBracket | 0 | 1.00 | FALSE | 13 | 20: 21750, 30: 13443, 40: 10914, -1: 9579 |
| gender | 9503 | 0.89 | FALSE | 2 | M: 43926, F: 34006 |
| patientStatus | 83666 | 0.04 | FALSE | 8 | Dec: 1600, Hos: 1266, Hom: 316, Dis: 283 |
| fct_pref | 1018 | 0.99 | FALSE | 47 | Tok: 27010, Osa: 10866, Kan: 7229, Aic: 5531 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| cluster | 0 | 1 | 0.03 | FAL: 85123, TRU: 2312 |
可視化
全体傾向
date <- x %>%
with(seq(range(dateAnnounced)[1], range(dateAnnounced)[2], by = "day")) %>%
as.data.frame() %>%
dplyr::rename(date = 1)
sec_scale <- 50
x %>%
dplyr::group_by(dateAnnounced) %>%
dplyr::summarise(n = n()) %>%
dplyr::left_join(date, ., by = c("date" = "dateAnnounced")) %>%
dplyr::mutate(n = ifelse(is.na(n), 0, n)) %>%
dplyr::mutate(cum = cumsum(n),
ma = zoo::rollmeanr(n, k = 7L, na.pad = TRUE)) %>%
print() %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity",
fill = "dark green", alpha = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
ggplot2::geom_line(ggplot2::aes(y = ma), colour = "dark red") +
ggplot2::scale_y_continuous(
name = "陽性確定数（日別）・7日間移動平均（濃赤折線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性確定数（折線）")
) +
ggplot2::labs(title = paste0("Covid19, Japan（@", Sys.time(), "）"),
subtitle = "", x = "日付")## `summarise()` ungrouping output (override with `.groups` argument)## date n cum ma
## 1 2020-01-15 1 1 NA
## 2 2020-01-16 0 1 NA
## 3 2020-01-17 0 1 NA
## 4 2020-01-18 0 1 NA
## 5 2020-01-19 0 1 NA
## 6 2020-01-20 0 1 NA
## 7 2020-01-21 0 1 0.1428571
## 8 2020-01-22 0 1 0.0000000
## 9 2020-01-23 0 1 0.0000000
## 10 2020-01-24 1 2 0.1428571
## 11 2020-01-25 1 3 0.2857143
## 12 2020-01-26 1 4 0.4285714
## 13 2020-01-27 0 4 0.4285714
## 14 2020-01-28 3 7 0.8571429
## 15 2020-01-29 1 8 1.0000000
## 16 2020-01-30 5 13 1.7142857
## 17 2020-01-31 1 14 1.7142857
## 18 2020-02-01 4 18 2.1428571
## 19 2020-02-02 0 18 2.0000000
## 20 2020-02-03 0 18 2.0000000
## 21 2020-02-04 2 20 1.8571429
## 22 2020-02-05 3 23 2.1428571
## 23 2020-02-06 0 23 1.4285714
## 24 2020-02-07 0 23 1.2857143
## 25 2020-02-08 1 24 0.8571429
## 26 2020-02-09 0 24 0.8571429
## 27 2020-02-10 2 26 1.1428571
## 28 2020-02-11 1 27 1.0000000
## 29 2020-02-12 1 28 0.7142857
## 30 2020-02-13 5 33 1.4285714
## 31 2020-02-14 10 43 2.8571429
## 32 2020-02-15 11 54 4.2857143
## 33 2020-02-16 6 60 5.1428571
## 34 2020-02-17 7 67 5.8571429
## 35 2020-02-18 12 79 7.4285714
## 36 2020-02-19 6 85 8.1428571
## 37 2020-02-20 10 95 8.8571429
## 38 2020-02-21 17 112 9.8571429
## 39 2020-02-22 25 137 11.8571429
## 40 2020-02-23 12 149 12.7142857
## 41 2020-02-24 11 160 13.2857143
## 42 2020-02-25 13 173 13.4285714
## 43 2020-02-26 19 192 15.2857143
## 44 2020-02-27 26 218 17.5714286
## 45 2020-02-28 18 236 17.7142857
## 46 2020-02-29 9 245 15.4285714
## 47 2020-03-01 14 259 15.7142857
## 48 2020-03-02 19 278 16.8571429
## 49 2020-03-03 20 298 17.8571429
## 50 2020-03-04 35 333 20.1428571
## 51 2020-03-05 32 365 21.0000000
## 52 2020-03-06 55 420 26.2857143
## 53 2020-03-07 46 466 31.5714286
## 54 2020-03-08 33 499 34.2857143
## 55 2020-03-09 28 527 35.5714286
## 56 2020-03-10 59 586 41.1428571
## 57 2020-03-11 56 642 44.1428571
## 58 2020-03-12 56 698 47.5714286
## 59 2020-03-13 38 736 45.1428571
## 60 2020-03-14 63 799 47.5714286
## 61 2020-03-15 32 831 47.4285714
## 62 2020-03-16 19 850 46.1428571
## 63 2020-03-17 50 900 44.8571429
## 64 2020-03-18 35 935 41.8571429
## 65 2020-03-19 40 975 39.5714286
## 66 2020-03-20 55 1030 42.0000000
## 67 2020-03-21 37 1067 38.2857143
## 68 2020-03-22 46 1113 40.2857143
## 69 2020-03-23 40 1153 43.2857143
## 70 2020-03-24 73 1226 46.5714286
## 71 2020-03-25 98 1324 55.5714286
## 72 2020-03-26 105 1429 64.8571429
## 73 2020-03-27 114 1543 73.2857143
## 74 2020-03-28 204 1747 97.1428571
## 75 2020-03-29 181 1928 116.4285714
## 76 2020-03-30 95 2023 124.2857143
## 77 2020-03-31 257 2280 150.5714286
## 78 2020-04-01 284 2564 177.1428571
## 79 2020-04-02 285 2849 202.8571429
## 80 2020-04-03 340 3189 235.1428571
## 81 2020-04-04 377 3566 259.8571429
## 82 2020-04-05 364 3930 286.0000000
## 83 2020-04-06 238 4168 306.4285714
## 84 2020-04-07 378 4546 323.7142857
## 85 2020-04-08 541 5087 360.4285714
## 86 2020-04-09 596 5683 404.8571429
## 87 2020-04-10 646 6329 448.5714286
## 88 2020-04-11 708 7037 495.8571429
## 89 2020-04-12 529 7566 519.4285714
## 90 2020-04-13 305 7871 529.0000000
## 91 2020-04-14 526 8397 550.1428571
## 92 2020-04-15 574 8971 554.8571429
## 93 2020-04-16 575 9546 551.8571429
## 94 2020-04-17 574 10120 541.5714286
## 95 2020-04-18 607 10727 527.1428571
## 96 2020-04-19 381 11108 506.0000000
## 97 2020-04-20 363 11471 514.2857143
## 98 2020-04-21 396 11867 495.7142857
## 99 2020-04-22 415 12282 473.0000000
## 100 2020-04-23 455 12737 455.8571429
## 101 2020-04-24 399 13136 430.8571429
## 102 2020-04-25 350 13486 394.1428571
## 103 2020-04-26 227 13713 372.1428571
## 104 2020-04-27 194 13907 348.0000000
## 105 2020-04-28 298 14205 334.0000000
## 106 2020-04-29 229 14434 307.4285714
## 107 2020-04-30 226 14660 274.7142857
## 108 2020-05-01 298 14958 260.2857143
## 109 2020-05-02 330 15288 257.4285714
## 110 2020-05-03 213 15501 255.4285714
## 111 2020-05-04 187 15688 254.4285714
## 112 2020-05-05 132 15820 230.7142857
## 113 2020-05-06 111 15931 213.8571429
## 114 2020-05-07 108 16039 197.0000000
## 115 2020-05-08 102 16141 169.0000000
## 116 2020-05-09 129 16270 140.2857143
## 117 2020-05-10 77 16347 120.8571429
## 118 2020-05-11 59 16406 102.5714286
## 119 2020-05-12 104 16510 98.5714286
## 120 2020-05-13 66 16576 92.1428571
## 121 2020-05-14 112 16688 92.7142857
## 122 2020-05-15 65 16753 87.4285714
## 123 2020-05-16 69 16822 78.8571429
## 124 2020-05-17 35 16857 72.8571429
## 125 2020-05-18 42 16899 70.4285714
## 126 2020-05-19 32 16931 60.1428571
## 127 2020-05-20 40 16971 56.4285714
## 128 2020-05-21 55 17026 48.2857143
## 129 2020-05-22 36 17062 44.1428571
## 130 2020-05-23 35 17097 39.2857143
## 131 2020-05-24 55 17152 42.1428571
## 132 2020-05-25 34 17186 41.0000000
## 133 2020-05-26 36 17222 41.5714286
## 134 2020-05-27 42 17264 41.8571429
## 135 2020-05-28 71 17335 44.1428571
## 136 2020-05-29 80 17415 50.4285714
## 137 2020-05-30 50 17465 52.5714286
## 138 2020-05-31 37 17502 50.0000000
## 139 2020-06-01 38 17540 50.5714286
## 140 2020-06-02 54 17594 53.1428571
## 141 2020-06-03 34 17628 52.0000000
## 142 2020-06-04 52 17680 49.2857143
## 143 2020-06-05 47 17727 44.5714286
## 144 2020-06-06 50 17777 44.5714286
## 145 2020-06-07 36 17813 44.4285714
## 146 2020-06-08 23 17836 42.2857143
## 147 2020-06-09 48 17884 41.4285714
## 148 2020-06-10 40 17924 42.2857143
## 149 2020-06-11 39 17963 40.4285714
## 150 2020-06-12 64 18027 42.8571429
## 151 2020-06-13 45 18072 42.1428571
## 152 2020-06-14 75 18147 47.7142857
## 153 2020-06-15 74 18221 55.0000000
## 154 2020-06-16 49 18270 55.1428571
## 155 2020-06-17 50 18320 56.5714286
## 156 2020-06-18 72 18392 61.2857143
## 157 2020-06-19 74 18466 62.7142857
## 158 2020-06-20 63 18529 65.2857143
## 159 2020-06-21 60 18589 63.1428571
## 160 2020-06-22 43 18632 58.7142857
## 161 2020-06-23 68 18700 61.4285714
## 162 2020-06-24 99 18799 68.4285714
## 163 2020-06-25 81 18880 69.7142857
## 164 2020-06-26 105 18985 74.1428571
## 165 2020-06-27 93 19078 78.4285714
## 166 2020-06-28 111 19189 85.7142857
## 167 2020-06-29 111 19300 95.4285714
## 168 2020-06-30 140 19440 105.7142857
## 169 2020-07-01 131 19571 110.2857143
## 170 2020-07-02 202 19773 127.5714286
## 171 2020-07-03 242 20015 147.1428571
## 172 2020-07-04 273 20288 172.8571429
## 173 2020-07-05 209 20497 186.8571429
## 174 2020-07-06 174 20671 195.8571429
## 175 2020-07-07 213 20884 206.2857143
## 176 2020-07-08 209 21093 217.4285714
## 177 2020-07-09 355 21448 239.2857143
## 178 2020-07-10 430 21878 266.1428571
## 179 2020-07-11 386 22264 282.2857143
## 180 2020-07-12 410 22674 311.0000000
## 181 2020-07-13 263 22937 323.7142857
## 182 2020-07-14 331 23268 340.5714286
## 183 2020-07-15 456 23724 375.8571429
## 184 2020-07-16 622 24346 414.0000000
## 185 2020-07-17 597 24943 437.8571429
## 186 2020-07-18 670 25613 478.4285714
## 187 2020-07-19 512 26125 493.0000000
## 188 2020-07-20 413 26538 514.4285714
## 189 2020-07-21 635 27173 557.8571429
## 190 2020-07-22 796 27969 606.4285714
## 191 2020-07-23 982 28951 657.8571429
## 192 2020-07-24 779 29730 683.8571429
## 193 2020-07-25 805 30535 703.1428571
## 194 2020-07-26 835 31370 749.2857143
## 195 2020-07-27 599 31969 775.8571429
## 196 2020-07-28 980 32949 825.1428571
## 197 2020-07-29 1269 34218 892.7142857
## 198 2020-07-30 1302 35520 938.4285714
## 199 2020-07-31 1586 37106 1053.7142857
## 200 2020-08-01 1535 38641 1158.0000000
## 201 2020-08-02 1332 39973 1229.0000000
## 202 2020-08-03 965 40938 1281.2857143
## 203 2020-08-04 1243 42181 1318.8571429
## 204 2020-08-05 1360 43541 1331.8571429
## 205 2020-08-06 1491 45032 1358.8571429
## 206 2020-08-07 1610 46642 1362.2857143
## 207 2020-08-08 1570 48212 1367.2857143
## 208 2020-08-09 1447 49659 1383.7142857
## 209 2020-08-10 842 50501 1366.1428571
## 210 2020-08-11 704 51205 1289.1428571
## 211 2020-08-12 984 52189 1235.4285714
## 212 2020-08-13 1183 53372 1191.4285714
## 213 2020-08-14 1363 54735 1156.1428571
## 214 2020-08-15 1240 55975 1109.0000000
## 215 2020-08-16 1032 57007 1049.7142857
## 216 2020-08-17 656 57663 1023.1428571
## 217 2020-08-18 930 58593 1055.4285714
## 218 2020-08-19 1080 59673 1069.1428571
## 219 2020-08-20 1195 60868 1070.8571429
## 220 2020-08-21 1053 61921 1026.5714286
## 221 2020-08-22 989 62910 990.7142857
## 222 2020-08-23 754 63664 951.0000000
## 223 2020-08-24 504 64168 929.2857143
## 224 2020-08-25 729 64897 900.5714286
## 225 2020-08-26 907 65804 875.8571429
## 226 2020-08-27 881 66685 831.0000000
## 227 2020-08-28 888 67573 807.4285714
## 228 2020-08-29 856 68429 788.4285714
## 229 2020-08-30 613 69042 768.2857143
## 230 2020-08-31 445 69487 759.8571429
## 231 2020-09-01 646 70133 748.0000000
## 232 2020-09-02 606 70739 705.0000000
## 233 2020-09-03 667 71406 674.4285714
## 234 2020-09-04 602 72008 633.5714286
## 235 2020-09-05 610 72618 598.4285714
## 236 2020-09-06 457 73075 576.1428571
## 237 2020-09-07 303 73378 555.8571429
## 238 2020-09-08 529 73907 539.1428571
## 239 2020-09-09 522 74429 527.1428571
## 240 2020-09-10 717 75146 534.2857143
## 241 2020-09-11 653 75799 541.5714286
## 242 2020-09-12 659 76458 548.5714286
## 243 2020-09-13 446 76904 547.0000000
## 244 2020-09-14 276 77180 543.1428571
## 245 2020-09-15 539 77719 544.5714286
## 246 2020-09-16 562 78281 550.2857143
## 247 2020-09-17 500 78781 519.2857143
## 248 2020-09-18 580 79361 508.8571429
## 249 2020-09-19 603 79964 500.8571429
## 250 2020-09-20 484 80448 506.2857143
## 251 2020-09-21 318 80766 512.2857143
## 252 2020-09-22 336 81102 483.2857143
## 253 2020-09-23 224 81326 435.0000000
## 254 2020-09-24 493 81819 434.0000000
## 255 2020-09-25 582 82401 434.2857143
## 256 2020-09-26 646 83047 440.4285714
## 257 2020-09-27 488 83535 441.0000000
## 258 2020-09-28 311 83846 440.0000000
## 259 2020-09-29 538 84384 468.8571429
## 260 2020-09-30 581 84965 519.8571429
## 261 2020-10-01 645 85610 541.5714286
## 262 2020-10-02 553 86163 537.4285714
## 263 2020-10-03 587 86750 529.0000000
## 264 2020-10-04 402 87152 516.7142857
## 265 2020-10-05 283 87435 512.7142857## Warning: Removed 6 row(s) containing missing values (geom_path).
plotly::ggplotly()